A Markov Chain is a probabilistic model used to describe a sequence of events where the probability of each event depends only on the previous event.
A Markov Chain works by assigning probabilities to each possible event in a sequence based on the previous event. This allows for the prediction of future events based on the current state of the system.
Markov Chains have been used in various fields such as finance, genetics, speech recognition, text prediction, and weather forecasting.
Markov Chains assume that the future state of a system depends only on the current state, making it unable to account for long-term dependencies or external factors. Additionally, it can only be applied to systems with a finite set of possible states.
Unlike other models that take into account historical data, Markov Chains only consider the current state, making it a memoryless model. It also differs from other probabilistic models in that it can only have one state at a time.